Méthode d’opérationnalisation de mesures de la performance sensibles aux soins infirmiers basées sur des données de routine
Bibliographic record
Abstract
Introduction: The operationalization of nursing-sensitive performance measures has been highly variable. It results in measures that are sometimes suboptimal and difficult for managers and nurses to access. The objective is to propose a rigorous method for operationalizing nurse-sensitive performance measures based on routine data. Source of Information: The primary source of information for this article is an operationalization method adapted from a reporting guide and performance measure evaluation instrument. It includes 7 processes and 33 interrelated quality attributes. The application of this operationalization method was successfully tested in a university hospital. Discussion: Operationalization of nursing-sensitive performance measures is a complex process. This method is an original proposal that allows for the justification and argumentation of the choices made. We discuss how this method is a response to 3 methodological issues: (1) heterogeneous and poorly detailed operationalization methods; (2) critical attributes (e.g., relevance, scientific validity, feasibility) that lack consensus and (3) heterogeneous data architecture models. Implication and conclusion: This operationalization method provides a systematic and transparent approach to generating nursing-sensitive performance measures from routine data. It could improve their operationalization, facilitate their understanding and evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.171 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".